[Cherry pick] Support feed single persistable variable to PE (#19435)

* update executor feed

paddle/fluid/framework/parallel_executor.cc

@@ -200,12 +200,17 @@ class ParallelExecutorPrivate {
     InitNCCLCtxs(scope, bst);
   }
 #endif
+  inline bool IsPersistable(const std::string &name) const {
+    auto iter = is_persistable_.find(name);
+    return iter != is_persistable_.end() && iter->second;
+  }
 
   BuildStrategy build_strategy_;
   std::vector<platform::Place> places_;
   std::vector<Scope *> local_scopes_;
   Scope *global_scope_;  // not owned
   std::unique_ptr<details::SSAGraphExecutor> executor_;
+  std::unordered_map<std::string, bool> is_persistable_;
 
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
   platform::NCCLCommunicator *nccl_ctxs_{nullptr};
@@ -473,6 +478,8 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
       var_infos.back().name_ = node->Var()->Name();
       var_infos.back().type_ = node->Var()->GetType();
       var_infos.back().persistable_ = node->Var()->Persistable();
+      member_->is_persistable_.emplace(node->Var()->Name(),
+                                       node->Var()->Persistable());
     }
   }
 
@@ -642,15 +649,19 @@ void ParallelExecutor::FeedTensorsIntoLocalScopes(
 
 void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
     const std::unordered_map<std::string, LoDTensor> &tensors) {
-  for (auto pair : tensors) {
+  size_t num_places = member_->places_.size();
+  for (auto &pair : tensors) {
+    bool is_persistable = member_->IsPersistable(pair.first);
+    VLOG(3) << "Split " << (is_persistable ? "persistable" : "no persistable")
+            << " data (" << pair.first << "), dim:" << pair.second.dims()
+            << ", place: " << pair.second.place();
     auto lod_tensors = pair.second.SplitLoDTensor(member_->places_);
-    if (member_->places_.size() != lod_tensors.size()) {
-      bool is_cpu_place = platform::is_cpu_place(member_->places_.front());
+    bool is_cpu_place = platform::is_cpu_place(member_->places_.front());
+    if (!is_persistable && num_places != lod_tensors.size()) {
       auto error_info = string::Sprintf(
-          "The number(%d) of samples of "
-          "current batch is less than the count(%d) of "
-          "devices(%s), currently, it is not allowed. ",
-          lod_tensors.size(), member_->places_.size(),
+          "The number(%d) of samples[%s] of current batch is less than the "
+          "count(%d) of devices(%s), currently, it is not allowed. ",
+          lod_tensors.size(), pair.first, num_places,
           (is_cpu_place ? "CPU" : "GPU"));
       if (is_cpu_place) {
         error_info +=
@@ -658,7 +669,38 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
             "to determine the number of devices you need.";
       }
       PADDLE_THROW(error_info);
+    } else if (is_persistable) {
+      if (lod_tensors.size() == 1) {
+        lod_tensors.reserve(num_places);
+        auto &tensor = lod_tensors.front();
+        PADDLE_ENFORCE_EQ(tensor.dims(), pair.second.dims(),
+                          "The dim doesn't match.");
+        PADDLE_ENFORCE_EQ(tensor.place(), member_->places_.at(0),
+                          "The place doesn't match.");
+        for (size_t i = 1; i < num_places; ++i) {
+          lod_tensors.emplace_back();
+          auto &tmp = lod_tensors.back();
+          framework::TensorCopy(pair.second, member_->places_.at(i), &tmp);
+        }
+      }
+      if (lod_tensors.size() != num_places) {
+        auto error_info = string::Sprintf(
+            "The number(%d) of samples[%s] of the current batch does not match "
+            "the count(%d) of devices(%s). Because that %s is a persistable "
+            "variable, you can feed just one sample, in that case, the input "
+            "sample will be copied in %d copies and be sent to different "
+            "places separately. If you need that different place has different "
+            "value, you should feed %d samples.",
+            lod_tensors.size(), pair.first, num_places,
+            (is_cpu_place ? "CPU" : "GPU"), pair.first, num_places, num_places);
+        PADDLE_THROW(error_info);
+      }
     }
+    PADDLE_ENFORCE_EQ(
+        lod_tensors.size(), num_places,
+        "The number(%d) of samples of the current batch does not match the "
+        "count(%d) of devices.",
+        lod_tensors.size(), num_places);
     for (size_t j = 0; j < member_->places_.size(); ++j) {
       // TODO(panxy0718): Do I need to delete this var?
       auto t =

python/paddle/fluid/executor.py

@@ -496,8 +496,11 @@ class Executor(object):
                 feed_tensor = feed[feed_name]
                 if not isinstance(feed_tensor, core.LoDTensor):
                     feed_tensor = core.LoDTensor()
-                    # always set to CPU place, since the tensor need to be splitted
+                    # always set to CPU place, since the tensor need to be split
                     # it is fast in CPU
+                    assert isinstance( feed[feed_name], np.ndarray ), \
+                        "The input({}) should be numpy.array, but not {}.".format(
+                        feed_name, type(feed[feed_name]))
                     feed_tensor.set(feed[feed_name], core.CPUPlace())
                 feed_tensor_dict[feed_name] = feed_tensor
 
@@ -518,6 +521,9 @@ class Executor(object):
                     tensor = each[feed_name]
                     if not isinstance(tensor, core.LoDTensor):
                         tmp = core.LoDTensor()
+                        assert isinstance(each[feed_name], np.ndarray), \
+                            "The input({}) should be numpy.array, but not {}.".format(
+                            feed_name, type(each[feed_name]))
                         tmp.set(tensor, program._places[i])
                         tensor = tmp
                     res_dict[feed_name] = tensor

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -230,5 +230,6 @@ if(WITH_DISTRIBUTE)
 endif()
 
 set_tests_properties(test_recordio_reader test_parallel_executor_test_while_train test_parallel_executor_mnist
+        test_parallel_executor_feed_persistable_var
         test_parallel_executor_seresnext test_parallel_executor_crf test_sync_batch_norm_op
         PROPERTIES LABELS "RUN_TYPE=DIST")

python/paddle/fluid/tests/unittests/test_parallel_executor_feed_persistable_var.py

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+from functools import partial
+import numpy
+import unittest
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from simple_nets import init_data, simple_fc_net
+import os
+
+
+class TestFeedPersistableVar(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        os.environ['CPU_NUM'] = str(4)
+        batch_size = 4
+        cls.img, cls.label = init_data(
+            batch_size, img_shape=[784], label_range=9)
+        cls.feed_dict = {
+            'image': cls.img,
+            'label': cls.label,
+            'learning_rate': numpy.array([1.0]).astype("float32")
+        }
+
+    def optimizer(self):
+        learning_rate = fluid.layers.create_global_var(
+            name="learning_rate",
+            shape=[1],
+            value=1.0,
+            dtype='float32',
+            persistable=True)
+        optimizer = fluid.optimizer.SGD(learning_rate=learning_rate)
+        return optimizer
+
+    def check_feed_persistable_var(self, feed_dict, use_cuda=False):
+        if use_cuda and not core.is_compiled_with_cuda():
+            return
+        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(main, startup):
+            loss = simple_fc_net()
+
+            optimizer = self.optimizer()
+            optimizer.minimize(loss)
+
+            exe.run(program=startup)
+            compiled_prog = fluid.compiler.CompiledProgram(
+                main).with_data_parallel(loss_name=loss.name)
+
+            exe.run(program=compiled_prog, feed=feed_dict)
+
+    def test_feed_persistable_var(self):
+        self.check_feed_persistable_var(self.feed_dict)
+        self.check_feed_persistable_var(self.feed_dict, use_cuda=True)
+
+        self.feed_dict['learning_rate'] = numpy.array(
+            [1.0, 1.0]).astype("float32")
+        self.check_feed_persistable_var(self.feed_dict, use_cuda=True)
+
+
+if __name__ == '__main__':
+    unittest.main()